NEXUS BETWEEN ECONOMIC VOLATILITY, TRADE OPENNESS …

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NEXUS BETWEEN ECONOMIC VOLATILITY, TRADE OPENNESS AND FDI: AN APPLICATION OF ARDL, NARDL AND ASYMMETRIC CAUSALITY

Md. Qamruzzaman1+

Salma Karim2

1Associate Professor, School of Business and Economics, United International University, Dhaka, Bangladesh.

2Professor, School of Business and Economics, United International University, Dhaka, Bangladesh.

(+ Corresponding author)

ABSTRACT Article History Received: 6 April 2020 Revised: 22 May 2020 Accepted: 25 June 2020 Published: 15 July 2020

Keywords Foreign direct investments Economic volatility Trade openness Nonlinear unit roots Nonlinear autoregressive Distributed lag Asymmetry causality South Asia.

JEL Classification: O40; Q32; Q43.

The motivation behind this study is to examine whether regional economic volatility and trade openness has influenced the pattern of foreign direct investment (FDI) inflows in select South Asian countries during the period 1975–2019. In order to do so, we applied several nonlinear tests, including the unit root test, ordinary least squares (OLS) test, autoregressive distributed lag (NARDL) test, and causality test. The findings of the nonlinear unit root test suggested that the variables are stationary at the first deviation, following nonlinear systems. Furthermore, the presence of nonlinearity in empirical estimations has been proven through nonlinear OLS and Brock Dechert Scheinkman (BDS) tests. Referring to the results of the Wald test with NARDL, a long-term asymmetrical relationship between the research variables has been confirmed, and long-term and short-term asymmetry was also observed in all tested empirical models. Furthermore, the results of the study on directional causality and asymmetric assumption support the feedback hypothesis in explaining the directional causality between regional economic volatility, trade openness, and inflows of FDI.

Contribution/ Originality: To the best of our knowledge, this is the first empirical study that has investigated

the asymmetrical relationship between regional economic volatility, trade openness, and inflows of FDI in South

Asian countries by applying NARD and asymmetric directional causality tests. It has been established that a long-

term asymmetrical relationship exists between research variables and bidirectional causal effects. These findings are

in line with the common perception that less volatility and trade internationalization increases foreign capital flows

in the economy.

1. INTRODUCTION

In recent decades, incoming foreign direct investment (FDI) in developing countries has emerged as one of the

essential sources of domestic capital and long-term investment. During the period between 1990 and 2018, the

global stock of FDI increased elevenfold, from $2.2 trillion to $25 trillion; however, the global GDP grew threefold

during the same period (Taguchi & Yining, 2017). The propositions for FDI-led economic growth, especially in

developing countries, is well documented in empirical literature (See: Abdouli & Hammami, 2017; Ayanwale, 2007;

Asian Economic and Financial Review ISSN(e): 2222-6737 ISSN(p): 2305-2147 DOI: 10.18488/journal.aefr.2020.107.790.807 Vol. 10, No. 7, 790-807. © 2020 AESS Publications. All Rights Reserved. URL: www.aessweb.com

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Asian Economic and Financial Review, 2020, 10(7): 790-807

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Azman-Saini, Law, & Ahmad, 2010; Flora & Agrawal, 2017; Karimi & Yusop, 2009; Nistor, 2014; Tekin, 2012;

Tiwari & Mutascu, 2011; Wijeweera, Villano, & Dollery, 2010). It has been suggested that the contributory role

played by FDI in the process of economic growth is multifold, as it involves knowledge sharing, technology

transferals, market expansion, financial market development, and capital formation. It is, therefore, understandable

why developing nations persistently seek foreign investment, either in the form of long-term capital investment or

ownership claims, which are commonly known as equity investments.

There is a widespread belief among international institutions, academics, policymakers, and researchers that

FDI has a huge positive impact on the economic growth of developing countries. FDI plays a major role in

economic expansion when there is a shortage of domestic savings (Ali & Malik, 2017; Sokang, 2018). FDI has,

therefore, emerged as the most important external resource in developing countries over recent years, and it has

become a significant part of the capital formation in these countries, despite the fact that their share in the global

distribution of FDI is minimal and is potentially in decline (Younus, Sohail, & Azeem, 2014).

On the other hand, a growing number of empirical studies have explored the key determinants that stimulate

incoming FDI in the host country (See: Ali & Guo, 2005; Dellis, Sondermann, & Vansteenkiste, 2017; Ibrahim,

2019; Kok & Ersoy, 2009; Nunnenkamp, 2002; Obwona, 2001; Qamruzzaman, 2015; Rolfe, Ricks, Pointer, &

McCarthy, 1993; Sajilan, Umar Islam, & Anwar, 2019; Vijayakumar, Sridharan, & Rao, 2010; Wach &

Wojciechowski, 2016). From an empirical perspective, it is evident that studies have either concentrated on specific

countries, explored the key determinants of incoming FDI, or explored the FDI-economic growth nexus using

different econometric techniques. However, regional effects on incoming FDIs have not yet been comprehensively

investigated. Therefore, the motivation of this study was to discover new insights in order to answer the following

question: Do regional macroeconomic fundamentals, namely economic growth volatility, trade openness, financial

development, and gross capital formation, influence incoming FDI? Certain aspects of this study are innovative; for

example, to the best of our knowledge, this is the first empirical study that has investigated regional

macroeconomic fundamentals and how they relate to inflows of FDIs in select South Asian countries between 1970–

2018. Second, in terms of investigation method, we applied the nonlinear unit root test proposed by Kapetanios,

Shin, and Snell (2006), the nonlinear ordinary least squares (OLS) and nonlinear autoregressive distributed lag

(ARDL) tests proposed by Shin, and the directional causality was investigated by applying an asymmetry causality

test proposed by Hatemi-J (2012).

From the findings of the study, we observed that the research variables followed a nonlinear stationary process.

Furthermore, nonlinear tests that follow nonlinear OLS and Brock Dechert Scheinkman (BDS) test statistics have

determined the fact that a nonlinear relationship exists between incoming FDI in select South Asian countries and

the behavior of regional macroeconomic fundamentals. An asymmetry causality test established a unidirectional

causality in the South Asian economy running from positive shocks in regional economic growth to positive shocks

in incoming FDI, which the feedback hypothesis had also revealed.

The structure of this paper is as follows: Section II presents a summary of the relevant literature. The variable

definitions and econometric methodologies are explained in detail in Section III. Section IV contains the empirical

model results and interpretations, and the conclusion is presented in Section V.

2. LITERATURE REVIEW

The nature of the relationship between FDI and economic growth is not clearly understood, particularly as it

pertains to regional outputs and employment. The theoretical and empirical literature has identified some of the

preconditions that are necessary in order for FDI to stimulate national growth, although their importance may vary

dramatically by region. Although the mechanisms through which FDI stimulates regional economies have largely

been ignored, new growth theories suggest that FDI has played a pivotal role.


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Regional and interregional economic integration efforts are an important feature of today‘s economic landscape,

and they have a direct impact on FDI flows. The motivation behind such efforts is to accelerate and expedite the

process of FDI by opening up investment liberalization and market integration through direct cooperation on

investment projects in host countries. In Te Velde and Bezemer‘s (2006) study, they argue that interregional

integration increases regional trade and inflows of FDI in the host country. Furthermore, regional integration

accelerates economic growth and increases investment.

FDI has played a crucial role in the formation of supply chains and production networks in developing

countries. Involvements in economic integration should improve an economy‘s business environment, which, in

turn, makes a country a more interesting prospect for foreign investors. FDI pertains to international investment in

which the investor obtains a lasting interest in an enterprise in another country. Involvement in supranational

economic structures significantly lowers transaction costs between foreign production and exportation.

Over the past three decades, the Asian economy—South Asian countries in particular—have experienced

substantial economic growth from FDI contributions. With regard to economic progress, the role of FDI in

industrialization is evident. Furthermore, empirical studies also examine how FDI stimulates capital accumulation,

technology transferals, knowledge sharing, human capital development, and international trade expansions (Bende‐

Nabende, Ford, & Slater, 2001).

From a theoretical perspective, if we follow the traditional neoclassical growth model, FDI merely increases the

investment rate, resulting in a transitional growth per capita, under the assumption that technological progress is

exogenous. Under the new ―endogenous‖ growth theory in which technological progress is endogenous, however,

FDI is considered to have a permanent growth effect through technological transfers and spillovers.

Tuluce, Dedeoglu, and Yaprak (2018) have investigated the role of the regional economic performance of Black

Sea Economic Cooperation (BSEC) countries on incoming FDIs during the 1994–2013 period using data from nine

countries. Fixed effects econometrics techniques and random effects models were applied when exploring the key

determinants that stimulate inflows of FDI in BSEC countries. The study found that regional economic growth,

market size, and exchange rates significantly affected inflows of FDI. The effects of regional economic growth on

inflows of FDI were positively established (See: Guerin & Manzocchi, 2009; Jaumotte, 2004; Te Velde & Bezemer,

2006). Regional economic growth intensifies regional integration, the mitigation of trade barriers, and the

optimization of economic resources and investment flexibility. Furthermore, regional integration can lead to more

extra-regional investment, which may not lead to more FDI in each member country.

Moudatsou and Kyrkilis‘ (2011) study focused on the relationship between FDI-led regional economic growth

in European Union (EU) and Association of Southeast Asian Nations (ASEAN) countries by carrying out causality

tests for the 1970-2003 period. The findings of the study presented growth-driven FDI in EU countries and

feedback hypothesis in ASEAN countries. As a result, they suggested that regional economic growth and country-

specific economic performance play a critical role in acquiring FDI into the economy.

Investment rules govern cross-border investments in the region in order to regulate the treatment and

protection of FDI, which subsequently contributes to the ―investment climate‖. FDI inflows are foreign investments

that allow host countries to reduce transaction costs and enhance domestic market expansion. Dennis (2006)

conducted a study in order to explain the role of trade liberalization on encouraging inward FDI in the Middle East

and North Africa (MENA). The findings revealed that trade liberalization strengthens the process of economic

integration and employment generation. The consequent effect presented in the study is that welfare contributions

from FDI inflows increase substantially.

Another group of findings in the empirical literature focus on the nexus between regional economic growth and

inflows of FDI (See: Bende‐Nabende et al., 2001; Brock, 2005; Changbiao, 2010; Chen, Sheng, & Liu, 2010; Mullen

& Williams, 2005; Sun & Parikh, 2001). In Iwasaki and Suganuma‘s (2015) study, they assess the effects of regional


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economic growth on the inflow of FDI in 71 regions in Russia between 1996 and 2011 by applying System-GMM.

The findings of the study establish a long-term association between regional economic growth and inflows of FDI.

Further evidence in Chun-Chien and Chih-Hai‘s (2008) study commended FDI contributions for research and

development (R&D), as well as capital and technological sharing, which have significantly contributed towards the

sustainable economic growth of China, particularly in the long-term.

The effects of regional integration on FDI are also available in the literature. In the study of Te Velde (2011), it

is obvious that regional integration does not significantly influence the inflows of FDI. However, countries with

regional trade agreements experienced an increase in FDI, provided that trade liberalization and investment

provisions acted as a motivational factor to attract foreign investors.

3. METHODOLOGY AND VARIABLE DEFINITIONS

3.1. Variable Definitions

The annualized time series data was used to explore new insights covering 44 years of observations of the

period between 1975 and 2019. The variables used in the study are: Regional economic growth volatility (hereafter

Volatility), which is measured by the standard deviation of economic growth; regional trade openness, which is measured

by the sum of imports and exports in the South Asian economy; and the inflow of FDI, which is measured by FDI as

a percentage of the GDP of respective countries, which include Bangladesh, India, Pakistan, and Sri Lanka. The

selection of the country‘s sample depends purely on the availability of long-term data. All the research data were

extracted from world development indicators published by the World Bank and the observations were converted

into a natural logarithm.

3.2. Estimation Techniques

In the study, we performed several econometric techniques in order to unveil certain types of information.

When investigating variables and the order of integration, we applied traditional unit root tests: ADF (Dickey and

Fuller (1979), P-P (Phillips and Perron, 1988) and KPSS (Kwiatkowski, Phillips, Schmidt, and Shin, 1992),

assuming linear stationary processes and nonlinear unit root tests proposed by Kapetanios, Shin, and Snell (2003)

and Kruse (2011). Furthermore, the BDS (Broock, Scheinkman, Dechert, & LeBaron, 1996) nonlinearity test and the

nonlinear ordinary least squares (NOLS) estimation techniques were employed in order to establish the presence of

a nonlinear relationship between inflows of FDIs in selected South Asian countries and regional economic volatility,

trade openness, financial development, and capital formation. The coefficient of nonlinear positive and negative

shocks to regional growth volatility and trade openness and how they relate to inflows of FDI were estimated

through the application of a nonlinear ARDL test proposed by Shin, Yu, and Greenwood-Nimmo (2014). Finally,

the asymmetric causal relationship was also investigated following an asymmetry causality test proposed by

Hatemi-J (2012).

3.2.1. The Kapetanios et al. (2003) Test

There is a growing dissatisfaction with the standard linear Autoregressive Moving Average model (ARMA)

framework that investigators use to test unit roots (Kapetanios et al., 2003). Much of this arises from the fact that,

in several areas of economics, a theoretical prediction of stationarity is confounded in practice by the persistent

failure of the standard Dickey-Fuller (DF) test to reject the null of a unit root (Rose, 1988; Taylor, Peel, & Sarno,

2001). In order to resolve this issue that is related to the linear unit root test, Kapetanios et al. (2003) introduced an

alternative nonlinear exponential smooth transition autoregressive (ESTAR) process, which is comprehensively

stationary.


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Therefore, following Kapetanios et al. (2003), Liu and He (2010) and Galadima and Aminu (2020), this paper

specifies the ESTAR model as:

* ( )+ ( )

Where is the demeaned or detrended time series of interest, β and is an unknown parameter, the term

* ( )+ is the exponential transition function adopted to represent the nonlinear adjustment, and εt is

the stochastic term that is assumed to be normally distributed with a zero mean and a constant variance.

Hence, from Equation 1, we test the following hypotheses:

(2)

and

( )

Obviously, according to Davies (1987), directly testing the null hypothesis (1) is not feasible, since is not

identified under the null. Resolving this issue, Kapetanios et al. (2003) suggest applying (Luukkonen, Saikkonen, &

Teräsvirta, 1988) and deriving a t-type test statistic. In addition to the reparameterization of Equation 1 and the

obtaining of a first-order Talyor series approximation to the ESTAR model under the null, the auxiliary regression

is calculated.

( )

This suggests that it is easy to acquire the value of t-statistics for , as:

( ) ( )

Where is the ordinary least squares (OLS) estimate of d, and ( ) is the standard error of the^ d.

However, it is noteworthy that the the statistic does not follow an asymptotic standard normal distribution.

3.2.2. The Kruse (2011) Test

Kapetanios et al. (2003) proposed an ESTAR-based nonlinear unit root test with the assumption that the

location parameter ‗c‘ in the smooth transition function is equal to zero (See Equation 1 for empirical study), which

has become popular among researchers. However, a growing number of studies have observed the fact that the

coefficient of ‗c‘ is significant, such as Michael, Nobay, and Peel (1997), Sarantis (1999), Taylor et al. (2001), and

Rapach and Wohar (2006). Kruse (2011) has argued that the exclusion of basic assumptions leads to the

nonstandard testing problem. Therefore, in order to mitigate the location parameter issue, modified test statistics

are used (Abadir & Distaso, 2007). The modified ESTAR specification is represented in Equation 6.

* ( ( ) + ( )

Where ∼ iid(0, σ2). If the smoothness parameter γ approaches zero, the ESTAR model becomes a linear

AR(1) model, i.e. = + , which is stationary if −2 < α < 0 NOLS. Therefore, the modified ADF regress

can be represented in Equation 7.

∑

( )

In the equation, the null hypothesis becomes with the alternative hypothesis of

, where stems from the fact that the location parameter ‗c‘ is allowed to take nonzero values.

Therefore, according to Yıldırım (2017), a standard wild test is not appropriate for deriving test statistics, rather

Kruse (2011) proposed a modified Wald test, widely known as ―the Kruse test‖, by integrating the procedure

initiated by Abadir and Distaso (2007). That is presented in Equation 8.


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( ) ( )

3.2.3. Nonlinear Autoregressive Distributed Lagged (NARDL)

Over the past few years, the concept of nonlinearity between dependent and explanatory variables has become

one of the key aspects involved when assessing relationships in empirical investigations. In line with nonlinearity,

Shin et al. (2014) introduced a new nonlinear cointegration equation, which has become widely known as an

NARDL, by incorporating two sets of additional explanatory variables in the equation: positive and negative

shocks.

As this new proposed concept estimates both long-term and short-term, a growing number of empirical studies

have extensively applied this concept to their respective studies (See: Ali, Shan, Wang, & Amin, 2018;

Qamruzzaman & Jianguo, 2018a; Qamruzzaman & Jianguo, 2018b; Qamruzzaman & Jianguo, 2018c). The

breakdown of positive and negative shocks in explanatory variables can be computed using Equations 9 and

Equation 10.

{

( ) ∑

∑ ( )

( ) ∑ ∑ (

)

( )

{

( ) ∑

∑ ( )

( ) ∑ ∑ ( )

( )

Following Shin et al. (2014), the partial asymmetry cointegration equation can now be obtained by inserting

positive and negative shocks of the explanatory variable into the standard symmetric equation and the new

nonlinear ARDL, as follows:

∑

∑ ( )

∑ ( )

∑ ( )

∑ ( )

( )

( ) ( )

( ) ( )

In Equation 11, ‗m‘, ‗n‘ and ‗r‘ denote the optimal estimated lag length for a model. A standard Wald test will

then be performed in order to determine the long-term asymmetric effects, from financial development, trade

openness, FDI, inflation, and economic growth, using the following null hypothesis of symmetry:

(

) (

)

against the alternative hypothesis of asymmetry:

(

) (

)

The rejection of the null hypothesis confirms the existence of the asymmetrical effects, from regional economic

growth volatility and trade openness to inflows of FDI in the long term. Long-term elasticity can be computed

using:

;

;

;

.


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To investigate the existence of a long-term asymmetric relationship, Shin proposed a bound test, which is a

joint test of all lagged levels of regressors. The Wald F-test is utilized to test the null hypothesis if there is no

asymmetric relationship:

against the alternative hypothesis:

The rejection of the null hypothesis demonstrates a long-term and short-term asymmetric relationship between

financial development, trade openness, FDI, inflation, and economic growth in Bangladesh.

3.2.4. Asymmetry Causality Test by Hatemi-J (2012)

The investigation of an asymmetrical relationship between variables using an empirical test requires two

additional sets of data that represent the breakdown of a variable into cumulative positive and negative changes.

The initial idea of variable breakdown into positive and negative changes was initiated by Granger and Yoon (2002)

in their study that explored hidden cointegration tests. Hatemi-J (2012) built upon their work on causality analysis,

interpreting it as an asymmetry causality test. According to Hatemi-J (2012), the sense that positive and negative

changes may not produce similar effects on the dependent variable is asymmetry. Furthermore, Hatemi-J (2012)

initiated the causal investigation between two variables, namely and , using a random walk proposition, and

defined this relationship in Equation 12 and Equation 13:

∑ ( )

and

∑

( )

Where t=1, 2……T, the constants and are the initial values, and the values and explain the white

noise disturbance term. The positive and negative shocks can be computed as ( ),

( ), ( ) and

( ), respectively. Therefore, we can represent

, and

. So, Equation 12 and Equation 13 can be reproduced in Equation 14 and

Equation 15 through the integration of the broken down variables:

∑

∑

( )

and

∑

∑

( )

In order to capture the asymmetric effects of all the variables, the positive and negative shocks of all variables

can be computed using Equation 16:

∑

∑

∑

∑

( )

The next step is to investigate the causal relationship by applying a vector autoregressive (VAR) model

with an order of p. The innovative lag can be determined by following the process of Hatemi-J (2003; 2008);

Equation 17 can be employed to select the optimal lag length in a VAR situation:

(| |) ( ( )

) ( )


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where stands for the determinants of the VAR covariance matrix of the error correction term in the VAR

model, ‗q‘ stands for the lag order, ‗n‘ specifies the number of the equation, and ‗T‘ denotes the number of

observations.

4. EMPIRICAL MODEL ESTIMATIONS AND INTERPRETATIONS

4.1. Unit Root Test Results

Table 1 presents the results of the following commonly-used unit root tests: ADF (Dickey and Fuller, 1979), P-

P test (Phillips and Perron, 1988) when the null hypothesis of the variable has a unit root, as well as the KPSS test

(Kwiatkowski et al., 1992) when the null hypothesis of the variables has no unit root, including the assumption of

constants and trends. The results of the study have unveiled a mixed integration order, which implies that the

variables were stationary at one level and/or after the first difference.

Table 1. Linear unit root test

ADF P-P KPSS

With Constant

With Constant &

Trend With

Constant

With Constant &

Trend With

Constant With Constant

& Trend

-1.350 -3.056 0.116 -1.718 0.694** 0.188**

-1.218 -3.213* -1.274 -3.122 0.7641*** 0.1288*

5.3998 18.1695 -0.718 -4.468*** 0.8862*** 0.1668**

-1.375 -2.5676 -1.379 -2.393 0.7955*** 0.1297*

TO -2.768** -2.651 -3.24** -3.623** 0.121 0.080

-3.170** -3.146 -3.44** -3.793** 0.125 0.092

-8.885*** -8.895*** -3.069** -3.102 0.132 0.106

-8.605*** -8.663*** -6.678*** -5.432*** 0.2394 0.166**

-21.32*** -24.82*** -6.412*** -6.311*** 0.3434 0.1404*

-6.28*** -6.379*** -6.285*** -6.396*** 0.0692 0.0359

∆TO -6.230*** -6.282*** -6.599*** -6.498*** 0.173 0.173**

-7.338*** -7.572*** -3.999*** -4.016*** 0.088 0.088

Table 2 presents the results of the nonlinear unit root test following the procedure introduced by Kapetanios et

al. (2003). The tests were conducted with a constant [case 1] and trend. The test results of FDI inflows and

regional GDP volatility in Bangladesh, India, and Pakistan confirmed the presence of a nonlinear unit root by

rejecting the null hypothesis of the linear unit root test at a level of 1%, and a significance with the constant and

trend at a level of 5%. Furthermore, trade openness presented a nonlinear unit root test with 5% significance with a

constant.

Table 2. KSS nonlinear unit root test results

Series With Constant

With Constant & Trend

Foreign direct investment _Bangladesh -2.244 -3.239* Foreign direct investment _India -1.970 -4.963*** Foreign direct investment _Pakistan -1.021 -4.526*** Foreign direct investment _Sri Lanka -0.253 -0.506 GDP_volatility regional -6.306*** -3.228* Regional Trade Openness -3.405** 0.424 Critical value Kapetanios. et al. (2003) Case 1 Case 2 Case 3 1% -2:82 −3:48 −3:93 5% −2:22 −2:93 −3:40 10% −1:92 −2:66 −3:13

Note: The critical values were taken from Table 1, Kapetanios et al. (2003), the asymptotic critical values of Case 1, Case- 2, and Case 3 indications at level, constant, and constant and trend, respectively.


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Table 3 displays the results of the Kruse unit root test (2011). Taking into account the test statistics, the null

hypothesis of the linear unit root test was rejected by FDI inflows in Bangladesh, India, and Sri Lanka at having 1%

significance. Furthermore, trade openness and GDP volatility also rejected the null hypothesis at having 10%

significance. These findings imply that the following research variables—regional economic growth volatility, trade

openness, and FDI inflows in Bangladesh, India, and Sri Lanka—follow a nonlinear stationary process.

Table 3. Kruse nonlinear unit root test results

Series With Constant With Constant & Trend

Foreign direct investment _Bangladesh 2.420 20.078*** Foreign direct investment _India 15.212*** 20.469*** Foreign direct investment _Pakistan 4.786 8.013 Foreign direct investment _Sri Lanka 16.128*** 26.021*** GDP_volatility regional 9.927* 10.067 Regional Trade Openness 2.026 11.515* Asymptotic Critical Values of t-statistic Case 1 Case 2 Case 3 1% 13.15 13.75 17.10 5% 9.53 10.17 12.82 10% 7.85 8.60 11.10

4.2. Nonlinearity Test

Table 4 reports the NOLS estimates for the nonlinear model that follows a polynomial function of degree four,

which was found to be the most appropriate model given the information criteria. The null hypothesis of the linear

relationship was rejected at 1% significance, which implies that the relationship between regional economic growth

volatility, trade openness, and inflows of FDI in Bangladesh, India, Pakistan, and Sri Lanka is nonlinear.

Table 4. Nonlinear ordinary least Square (NOLS)

Bangladesh India Sri Lanka Pakistan

Variable Coff. Prob. Coff. Prob. Coff. Prob. Coff. Prob.

-6.979 0.019 -1.278 0.073 -2.220 0.654 -1.623 0.1977 TO 5.156 0.070 1.092 0.087 -9.587 0.0266 4.262 0.9961

( ) -11.19 0.076 4.991 0.057 3.004 0.4617 12.955 0.1443

( ) 86.693 0.070 -76.874 0.054 -23.42 0.3506 -12.541 0.1297

( ) -21.624 0.067 19.432 0.053 24.727 0.2778 32.912 0.1233 (TO)^2 -28.921 0.007 -2.140 0.085 19.831 0.0254 -0.538 0.9309 (TO)^3 1.110 0.008 0.102 0.081 -0.755 0.0269 0.037 0.8753 (TO)^4 -0.014 0.008 -0.001 0.078 0.0095 0.0285 -0.0069 0.8229 R-squared 0.894

0.951

0.934

0.911

Adj R-sq 0.861

0.937

0.915

0.885 F-statistic 27.308

67.869

50.515

35.457

74.514*** 11.714*** 11.947*** 16.570***

21.748*** 41.254*** 25.674*** 49.711*** Note: W indicates the standard Wald test results; *** indicates 1% significance.

We then chose to use the nonlinear BDS approach proposed by Broock et al. (1996) in order to detect the VAR

residuals. The null hypothesis in the BDS test with independent and identical distributions was rejected, which

means that the time series had nonlinear characteristics under different dimensions (m = 2, 3, ..., 6). As shown in

Table 5, the BDS test results indicate that the null hypothesis of linear dependence was rejected at a level of 1%,

demonstrating that the nonlinear model was more suitable for detecting short-term relationships between regional


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economic growth, trade openness, financial development, capital formation and inflows of FDI in certain South

Asian countries.

Table 5. Results of BDS statistics from VAR residuals

BDS Statistics

Dimension Y Volatility TO

2 0.061*** 0.188*** 3 0.120*** 0.311*** 4 0.151*** 0.398*** 5 0.166*** 0.457*** 6 0.161*** 0.491***

Note: *** indicates the significance of nonlinear dependency at a level of 1%.

Table 6. Dynamics of the long-term and short term estimations

Bangladesh India Pakistan Srilanka

Panel – A: Long-run model estimation

C -5.212***[7.694] -17.709***[0.435] -3.22***[0.872] -2.533***[0.504]

∆FDI(-1) -0.873***[0.409] -0.32***[0.095] -0.755***[0.068] 0.613***[0.082]

∆ (-1) 0.425***[0.729] 0.19***[0.038] 0.086***[0.981] 3.843**[0.633]

∆ (-1) 1.244***[0.82] 0.132***[0.276] 0.771**[0.222] -0.423***[0.829]

(-1) -4.945**[0.23] 2.371**[0.133] -0.696***[1.747] 0.088***[0.617]

(-1) 1.921***[0.633] -0.475**[0.004] 0.486***[0.107] 0.206***[0.392]

Panel –B: Short-run model estimation

∆FDI(-1) - -0.374**[0.188] -0.735***[0.241] -0.383**[0.192]

∆FDI(-2) -0.471**[1.359] -0.725**[0.288] -0.624***[0.206] -0.652***[0.112]

∆FDI(-3) 0.526***[0.823] -0.285**[0.322] -- --

∆ (-1) -1.301***[0.357] 8.291**[6.646] 5.733**[4.717] 13.838***[4.206]

∆ (-2) -1.302**[0.158] -5.863**[5.829] 7.931**[4.051] -7.427***[3.442]

∆ (-3) -1.652**[0.394] 10.618**[6.432] 7.423[3.804] 10.602**[3.524]

∆ (-1) -1.999**[0.604] -12.976**[5.537] -12.159*[4.149] -4.195***[2.016]

∆ (-2) -7.113**[0.943]

-10.755*[2.972] 1.213**[1.971]

∆ (-2) - -2.583*[3.04] -1.615**[2.133] -0.482***[14.082]

∆ (-1) - - -18.324*[5.495] -0.305**[15.029]

∆ (-2) 6.695*[2.802] - - - -1.871**[15.147]

∆ (-3) 2.007**[3.339] 4.954**[9.268] 15.501*[4.528] -2.595**[2.463]

∆ (-1) 1.437**[9.452] -5.02**[3.929] 7.337**[2.283] -7.772**[2.685]

∆ (-2) - 9.586**[4.407] 2.841**[2.062] 8.677*[1.625]

∆ (-3) -0.209*[3.202 - - 1.967**[0.514]

ECT(-1) -0.574(0.001) -0.721(0.002) -0.315(0.004) -0.351(0.009)

Fpass 13.772*** 12.294*** 9.494*** 6.645** Note: ***/**/* denote the level of significance as 1%/5%/10, respectively.

4.3. NARDL Estimations

We then proceeded to estimate the extent of the impact of regional economic growth volatility and trade

openness on the inflow of FDI by means of a nonlinear framework by using Equation 11. The original NARDL


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model results are presented in Table 6. We then proceeded to estimate the existence of a joint cointegration test in

the nonlinear Equation 11. We found test statistics that were higher than the upper boundary of the critical value1

of a 1% level of significance ( ( ) , ( ) , ( ) , and

( ) ), so that we could come to a conclusion in favor of an asymmetric association between

examined variables. Furthermore, the coefficients error correction term ( ) was observed in case all tested

models were negative and statistically significant at a 1% level of significance. This supports the previous

confirmation of a long-term cointegration.

Table 7. Long-term relationships

Model Estimation

Bangladesh India Pakistan Sri Lanka

Panel A: Long-term coefficient estimation

0.486***[0.729] 0.593***[0.037] 0.113***[0.981] -6.269**[0.633] 1.424**[0.819] 0.411**[0.260] 1.020**[0.222] 0.689**[0.828] -5.664***[0.229] 7.403***[0.301] -0.922**[0.174] -0.143***[0.609] 2.202**[0.633] -1.484***[0.995] 0.644***[0.107] -0.335**[0.389]

Panel B: Long-term and short term asymmetry test

14.521*** 25.741*** 22.861*** 16.971***

21.045** 16.852*** 11.907*** 9.813***

14.254*** 12.549*** 9.884*** 12.341***

9.754*** 8.214*** 8.741*** 22.624**

Note: ***/**/* denote the level of significance as 1%/5%/10, respectively.

Table 7 (Panel A) displays the results of the long-term positive and negative shocks of regional economic

growth volatility and trade openness on inflows of FDI in Bangladesh, India, Pakistan, and Sri Lanka. The

empirical model output establishes the fact that positive shocks in regional economic growth are positively linked to

inflows of FDI in select South Asian countries, except for Sri Lanka. Similarly, negative shocks in regional

economic growth volatility are positively associated with inflows of FDI in South Asian countries.

When considering positive and negative shocks in trade openness, we observed the fact that positive shocks in

trade openness produced negative results for Bangladesh (a coefficient of -5.664), Pakistan (a coefficient of -0.922),

and Sri Lanka (a coefficient of -0.143) in terms of receiving inflows of FDI. However, positive shocks in regional

trade openness were positively correlated to FDI inflows in India (a coefficient of 7.403). Furthermore, negative

shocks in regional trade openness were positively correlated with inflows of FDI in Bangladesh (a coefficient of

2.202) and Pakistan (a coefficient of 0.644). On the other hand, a negative correlation was also established in terms

of inflows of FDI in India (a coefficient of -1.484) and Sri Lanka (a coefficient of -0.335).

We also performed a standard Wald test in order to investigate the symmetrical relationship. In Table 7

(Panel B), indicates a Wald test with a null hypothesis of the long-term symmetrical relationship (

= and

= ), as well as a null hypothesis of the short-term symmetrical relationship (

=

and =

). Given that the results of the standard Wald test in both the long-term and short-term was a null

hypothesis, the symmetrical relationship was rejected at a 1% significance level. Therefore, it can be concluded that

there is a prevailing asymmetrical relationship between regional economic growth volatility, trade openness,

1 Following Shin et al. (2014), we adopted a conservative approach to the choice of critical values, employing k = 4


801


financial development, gross capital formation and incoming FDI in selected South Asian countries, both in the

short-term and long-term.

Finally, we ascertained the robustness and stability of the model‘s estimation by performing several residual-

based diagnostics tests. The results of the residual diagnostic test are exhibited in Table 8. The residual test shows

that the model was free from serial correlation, the error terms were distributed normally, and there were no

homoscedasticity problems. Moreover, Ramsey‘s RESET test also confirms the model‘s construction validity.

Estimations of the model‘s stability were investigated by applying a residual recursive test proposed by Pesaran,

Shin, and Smith (2001), commonly known as the CUSUM of the square recursive residual test, which determines

the model‘s stability by estimating whether the parameters have a 5% significance level (See Figures 1 to 8).

Table 8. Residual diagnostic test results

Empirical Model Estimation


0.744 0.721 0.651 0.633***

7.04*** 7.37*** 3.574*** 5.022***

1.97(0.197) 1.52(0.217) 1.541(0.451) 1.223(0.274)

0.45(0.951) 1.12(0.261) 0.974(0.714) 1.045(0.155)

21.64(0.292) 2.21(0.182) 3.511(0.774) 2.812(0.114)

0.52(0.263) 1.43(0.155) 1.114(0.524) 1.054(0.254)

CUSUM Figure 1 Figure 3 Figure 5 Figure 7 CUSUM of Square Figure 2 Figure 4 Figure 6 Figure 8

Figure 1. CUSUM test for Bangladash Figure 2. CUSUM of square test for Bangladash

Figure 3. CUSUM test for India

Figure 4. CUSUM of square test for India


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4.4. Asymmetric Causality Test

The results of the asymmetric causality test displayed in Table 9 include the positive and negative shocks of the

following independent variables: Regional economic growth volatility and trade openness. A number of causal

relationships have been revealed using the asymmetry test; however, we focused on explaining the effects of positive

and negative shocks on inflows of FDI in certain South Asian countries. With regard to Bangladesh, we observed a

prevailing bidirectional causality between positive shocks in regional economic growth and inflows of FDI

[ FDI]. Furthermore, unidirectional causality also appears when explaining the causal effects

between negative shocks in regional economic growth and inflows of FDI, as well as positive shocks in regional

trade openness and inflows of FDI. The findings of the study suggest that, in addition to Bangladesh‘s attributes,

inflows of FDI in Bangladesh are also guided by regional economic behavior.

With regard to India, the findings of the study disclosed bidirectional causality between negative shocks in

regional economic growth and inflows of FDI [ FDI]. In addition, unidirectional causal effects

occurred between positive shocks in regional economic growth and inflows of FDI [ FDI], as well as

positive shocks in trade openness and inflows of FDI [ FDI]. With regard to Pakistan, the findings of the

disclosed unidirectional causality between inflows of FDI and negative shocks in regional economic growth

[FDI

], as well as positive shocks in regional economic growth and inflows of FDI

[ FDI].

With regard to Sri Lanka, it is evident in the causality test results that there is bidirectional causality between

negative shocks in regional economic growth volatility and inflows of FDI [ FDI]. Furthermore,

Figure 5. CUSUM test for Pakistan

Figure 6. CUSUM of square test for Pakistan

Figure 7. CUSUM test for Sri Lanka

Figure 8. CUSUM of square test for Sri Lanka


803


unidirectional causality has been exposed in terms of positive shocks in regional economic growth

[ FDI] and trade openness [ FDI] on inflows of FDI.

Table 9. Asymmetric causality test


Null Hypothesis F-Stat Prob. F-Stat Prob. F-Stat Prob. F-Stat Prob.

FDI ≠ > Y_N 1.322 0.282 2.558 0.092 4.216 0.012 5.545 0.008 Y_N ≠ >FDI 4.444 0.025 4.179 0.023 1.779 0.184 3.314 0.048 FDI ≠ >Y_P 4.288 0.015 1.243 0.301 0.399 0.673 0.900 0.416 Y_P ≠ >FDI 3.331 0.050 3.781 0.018 4.451 0.024 9.503 0.000

FDI ≠ >TO_N 0.794 0.461 0.864 0.430 0.736 0.485 1.080 0.350

TO_N ≠ >FDI 0.384 0.684 0.170 0.844 0.882 0.422 2.448 0.101 FDI ≠ >TO_P 1.144 0.332 1.419 0.255 0.433 0.651 0.072 0.930 TO_P ≠ >FDI 3.162 0.057 4.316 0.011 1.740 0.189 8.880 0.000

TO_P ≠ >TO_N 3.488 0.041 3.488 0.041 3.488 0.041 3.488 0.041 TO_N ≠ >TO_P 0.594 0.557 0.594 0.557 0.594 0.557 0.594 0.557

Y_P ≠ >Y_N 10.704 0.000 10.704 0.000 10.704 0.000 10.704 0.000 Y_N ≠ >Y_P 0.918 0.408 0.918 0.408 0.918 0.408 0.918 0.408

5. CONCLUSION

Inflows of FDI are subject to certain macroeconomic fundamentals of the host economy, especially in

developing economies. In empirical studies, a growing number of researchers have attempted to explore and explain

these factors. However, the motivation of this study was to uncover new insights with regard to the role of regional

macroeconomic fundamentals in attracting FDI in the South Asian countries of Bangladesh, India, Pakistan, and Sri

Lanka. In this study, empirical evidence and a number of econometrical investigations have been established using a

nonlinear framework for the period between 1975 and 2019. The key findings of the empirical investigation are as

follows: First, in order to establish variables in order of integration, we performed both linear and nonlinear unit

root tests. According to the conventional unit root tests (ADF, P-P and KPSS), all the variables were stationary at

one level or after the first difference, and no variable established an order of integration after the second difference.

Nonlinear unit root tests were performed by following the tests of Kapetanios et al. (2003) and Kruse (2011). The

findings of the study revealed that, in Bangladesh, India, and Pakistan, the impact of regional economic growth

volatility, regional financial development, and regional gross domestic capital formation on FDI inflows follow the

nonlinear process and are stationary. After ascertaining nonlinearity in the empirical model, we performed a

nonlinear OLS and BDS test, and the findings confirmed nonlinearity. Second, in order to assess the magnitude of

regional economic growth volatility and trade openness in the nonlinear framework, we applied the nonlinear

framework proposed by Shin, breaking down the variables into positive and negative shocks. The remaining

asymmetries confirmed the presence of an asymmetric relationship between regional economic growth volatility,

trade openness, and incoming FDI. Third, the findings of the asymmetry causality test made it evident that

positive shocks in regional economic growth stimulated the inflow of FDI in Bangladesh, India, Pakistan, and Sri

Lanka. Furthermore, positive shocks in regional trade openness also accelerated inflows of FDI in these countries

during the period studied. The findings of this study have shown that, in addition to country-specific

macroeconomic fundamentals, regional economic activities are also important in terms of encouraging foreign

investors and increasing capital investments with foreign participants.

Funding: The study received a research grant from The Institute of Advanced Research (IAR), United International University (Grant Reference: UIU/IAR/02/2019-20/BE/14). Competing Interests: The authors declare that they have no competing interests. Acknowledgement: Authors are extremely grateful to the editor of this journal and two anonymous referees for their constructive comments on an earlier version of this manuscript.


804


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